Pedestrian Detection Using a Feature Space Based on Colored Level Lines

  • Pablo Negri
  • Pablo Lotito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

This work gives the guidelines to develop a pedestrian detection system using a feature space based on colored level lines, called Movement Feature Space (MFS). Besides detecting the movement in the scene, this feature space defines the descriptors used by the classifiers to identify pedestrians. The multi-channel level lines approach has been tested on the HSV color space, which improves the one-channel (gray scale) level lines calculation. Locations hypotheses of pedestrian are performed by a cascade of boosted classifiers. The validation of these regions of interest is carry out by a Support Vector Machine classifier. Results give more than 78.5 % of good detections on urban video sequences.

Keywords

Feature Space Video Sequence Color Space Level Line Monochromatic Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Alvarez, S., Salvatella, A., Vanrell, M., Otazu, X.: 3d texton spaces for color-texture retrieval. In: Image Analysis and Recognition, pp. 354–363 (2010)Google Scholar
  2. 2.
    Aubert, D., Guichard, F., Bouchafa, S.: Time-scale change detection applied to real-time abnormal stationarity monitoring. Real-Time Imaging 10, 9–22 (2004)CrossRefGoogle Scholar
  3. 3.
    Bouchafa, S.: Motion detection invariant to contrast changes. Application to detection abnormal motion in subway corridors. Ph.D. thesis, UPMC Paris VI (1998)Google Scholar
  4. 4.
    Cao, F., Musse, P., Sur, F.: Extracting meaningful curves from images. Journal of Mathematical Imaging and Vision 22, 1519–1581 (2005)CrossRefGoogle Scholar
  5. 5.
    Carron, T., Lambert, P.: Color edge detector using jointly hue, saturation, and intensity. In: ICIP, pp. 977–981 (1994)Google Scholar
  6. 6.
    Caselles, V., Col, I.B., Morel, J.: Topographic maps and local contrast changes in natural images. International Journal on Computer Vision 33, 5–27 (1999)CrossRefGoogle Scholar
  7. 7.
    Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm (accessed November 2011)
  8. 8.
    Comaniciu, D.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)CrossRefGoogle Scholar
  9. 9.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  10. 10.
  11. 11.
    Gouiffes, M., Zavidovique, B.: A color topographic map based on the dichromatic reflectance model. EURASIP JIVP, 1–14 (2008)Google Scholar
  12. 12.
    Negri, P., Clady, X., Hanif, S., Prevost, L.: A cascade of boosted generative and discriminative classifiers for vehicle detection. EURASIP JASP, 1–12 (2008)Google Scholar
  13. 13.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. 1, pp. 511–518 (December 2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pablo Negri
    • 1
    • 2
  • Pablo Lotito
    • 1
    • 3
  1. 1.CONICETCapital FederalArgentina
  2. 2.Instituto de TecnologiaUADECapital FederalArgentina
  3. 3.PLADEMA-UNCPBA, Campus UniversitarioTandilArgentina

Personalised recommendations